Discussing Indexing and Embedding Performance in Typesense
TLDR Dima had queries about indexing with embedding in Typesense. Kishore Nallan and Jason provided solutions, including reducing documents sent in an API call and running embeddings on a GPU. They facilitated Dima with the latest RC.
Jul 18, 2023 (4 months ago)
Kishore Nallan08:42 AM
But I faced with another problem: I ran re-indexing and found that while in-build embedding works on indexing it affects search performance. It is totally fine and expected, so I increased available CPU on the node 2 -> 4 -> 8 -> 16, but it’s still not enough. Can I decrease concurrency of embedding / indexing somehow? For now I’m sending 500 rows in one batch, maybe I should decrease this amount?
Indexed 3015 threads (79% resolved)
Issues with Cluster Upgrade and Embedding Field
Gustavo had issues upgrading their cluster and their embedding field wasn't being filled. Jason helped to solve the upgrade issue and advised re-indexing the documents to solve the embedding field issue. Both problems were successfully resolved.
Issues with Embeddings on Collection with 80K Documents
Samuel experienced issues when enabling embeddings on a large collection, leading to an unhealthy cluster. Kishore Nallan suggested rolling back to a previous snapshot, advised on memory calculations for OpenAI embeddings, and confirmed that creating a new cluster should solve the problem.
Utilizing Vector Search and Word Embeddings for Comprehensive Search in Typesense
Bill sought clarification on using vector search with multiple word embeddings in Typesense and using them instead of OpenAI's embedding. Kishore Nallan and Jason informed him that their development version 0.25 supports open source embedding models. They also resolved Bill's concerns regarding search performance, language support, and limitations in the search parameters.
Implementing Semantic Search with Typesense
Erik sought advice for semantic search implementation in Typesense and raised issues around slow document import and excessive latency. Upon implementing advice from Kishore Nallan to try different models, Erik reported faster times, ultimately deciding to rate-limit imports.
Optimum Cluster for 1M Documents with OpenAI Embedding
Denny inquired about the ideal cluster configuration for handling 1M documents with openAI embedding. Jason recommended a specific configuration, explained record size calculation, and clarified embedding generation speed factors and the conditions that trigger openAI.